Dr. Topol quotes studies suggesting that errors in interpretation of medical scans “are far worse than generally accepted,” with false positive rates of 2% and false negative rates over 25%. As a result, 31% of American radiologists have experienced a malpractice claim, “most of which were related to missed diagnoses.”

The rapid advances in computer vision due to the application of AI starting in 2012, have led to predictions of the imminent demise of radiologists, to be replaced by better diagnosticians—deep learning algorithms. Geoffrey Hinton, one of this year’s Turing Award winners and a major contributor to the astounding success of deep learning, suggested in 2016 that “People should stop training radiologists now. It’s just completely obvious that in five years deep learning is going to do better than radiologists.” In the same year, an article published in the Journal of the American College of Radiology warned that “The ultimate threat to radiology is machine learning. Machine learning will become a powerful force in radiology in the next 5 to 10 years and could end radiology as a thriving specialty.”

While Dr. Topol believes that eventually all medical scans will be read by machines, he argues that radiologists can have a bright future if they “adapt and embrace a partnership with machines.” Eyal Gura, co-founder and CEO of Zebra Medical Vision, agrees: “AI can help doctors get to the right place quickly and make the right decision.”

For the last 5 years, Zebra has helped doctors make the right decisions by developing deep learning algorithms for interpretation of medical images, working with data and research partners to train and improve the algorithms and validate their efficacy, and integrating Zebra’s products with the workflow of practicing radiologists.

Gura’s vision is that Zebra will help “automate every visual aspect of medicine,” going beyond radiology to pathology, dermatology, dentistry, and to all situations where “a doctor or a nurse are staring at an image and need to make a quick decision.” This “automation” does not mean replacing doctors. Rather, it means the augmentation of their work, providing consistent and accurate assistance. “We need all the doctors we have in the world and we will need 10X more because of the aging population,” says Gura.